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\begin{document}

\title{A novel efficient method of estimating suspended total sediment load
fraction in natural rivers}



\author[1]{Hyoseob Noh}%
\author[1]{Yong Sung Park}%
\author[1]{Il Won Seo}%
\affil[1]{Seoul National University}%


\vspace{-1em}



  
  \date{May 29, 2024}


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\begin{abstract}
Sediment transport load monitoring is important in civil and
environmental engineering fields. Monitoring the total load is
difficult, especially because of the cost of the bed load transport
measurement. This study proposes estimation models for the suspended
load to total load ratio (Fsus) using dimensionless hydro-morphological
variables. Two prominent variable combinations were identified using the
recursive feature elimination procedure of support vector regression
(SVR): (1) W/h, d*, Reh, Frd, and Rew and (2) Reh, Fr, and Frd. The
explicit interactions between Fsus and the two combinations were
revealed by two modern symbolic regression methods: multi-gene genetic
programming and Operon. The five-variable SVR model showed the best
performance (R2=0.7722). The target dataset was clustered by applying a
self-organizing map and Gaussian mixture model. Through these steps, Reh
and Frd are determined as the two most influential variables.
Subsequently, the one-at-a-time sensitivity of the input variables of
the empirical models was investigated. By referring to the clustering
and sensitivity analyses, this study provides physical insights into
Fsus controlling relationships. For example, Fsus is proportional to Reh
and is inversely related to Frd. The empirical models developed in this
study are applicable in practice and easy to implement in other
real-time surrogate suspended-sediment monitoring methods, because they
only require basic measurable hydro-morphological variables, such as
velocity, depth, width, and mean bed material grain size.%
\end{abstract}%



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